What’s New - BayesiaLab 11.7
Causal Network Generator: Improved Visuals & Modeling Capabilities
Color-Coded Node Classes
Specific colors are now automatically assigned based on the role of each node class:
- Interventions – green
- Root Causes – orange
- Main Criteria – blue
- Effects – yellow
For Risk-Centric Causal Networks:
- Threats – purple
- Preventive Barriers – green
- Mitigating Barriers – teal
- Main Risk – red
Automatic Divorcing
Automatically inserts intermediate nodes to “divorce” multiple parents and reduce CPT size explosion.
Leak Values as Constants
Leak values used in Switch-Noisy-Or and Dual-Noisy-Or functions can now be defined as constants, allowing global edits via the Constant Editor.
Risk-Centric Causal Networks: Optimization-Ready Modeling
Each Barrier (Preventive or Mitigating) can now be linked to a Utility Node.
The Main Risk can be linked to a Function Node computing the overall utility using the OverallUtility() function.
This structure supports Genetic Optimization for identifying the best risk mitigation strategy.
Text Import, Textual Dimension Elicitor & Textual Imputation
Text columns can now be imported as IID textual variables, for example customer feedback.
These variables support two new features:
- Textual Dimension Elicitor – performs concept detection directly from text
- Textual Imputation – estimates presence or absence of modeled concepts for each row, based on the text
Network Import from JSON
BayesiaLab now supports importing networks from JSON files.
These JSON structures are compatible with external LLMs and with HellixMap.
Layout Enhancements: ELK Positioning Algorithm
A new layout option based on the ELK (Eclipse Layout Kernel) algorithm is now available.
Two layout modes are supported: Vertical and Horizontal.
Ideal for clean and structured display of large networks, especially Risk-Centric Causal Networks.
Previous Releases
BayesiaLab 11.6
Version 11.6 of BayesiaLab is the latest iteration of our flagship product that has been under continuous development for nearly 25 years. No other organization has invested as many resources in developing technologies around the Bayesian network paradigm.
Release 11.6 once again features many innovations, including the native integration of a LLM-based subject matter assistant (OpenAI, OpenAI GPT Assistants, Azure, Mistral, Anthropic, Google AI, Llama API, DeepSeek, Voyage AI).
This page provides an overview of all the new features introduced in BayesiaLab 11, covering versions 11.0 through 11.6. Only the features specific to version 11.6 are marked with the icon .
New Features in BayesiaLab 11.6
Speech-to-Text
Press the mic icon associated with any of BayesiaLab’s text fields and use the Speech-to-Text feature to enter your text (node names, comments, …).
Additionally, the mic icon in the main toolbar allows you to search BayesiaLab’s functions using voice commands, making it easier to quickly find and access the features you need.
Independence of Causal Influence
The Independence of Causal Influence (ICI) tool has been enhanced with several updates:
New Combination Functions
SumPos(): An asymmetrical variation of the Sum function focusing on positive local mechanical effects.SumNeg(): A counterpart that emphasizes negative local mechanical effects.MinMax(): A function that implements the min method for negative values and the max method for positive ones.
ICI Wizard Enhancement
- A Condensed Display option has been introduced. This feature creates a network where the local effects are snapped to their parent and the combination nodes to their respective children.
Expert Knowledge Elicitation-Related Features:
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The Expert Editor has been renamed as the SMEs & BEKEE Session Manager.
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Subject Matter Experts (SMEs) can now be identified with specific colors for better differentiation.
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There’s an option to to send out invitation emails to the SMEs.
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In terms of qualitative knowledge elicitation, specifically the qualitative segment of the Delphi Method, you can now utilize Generate Notes from Comments in the Assessment Editor to produce Notes directly on the Graph Panel, derived from expert comments collected in BEKEE.
Additionally, a Note Report feature is now available, allowing access to all note contents in an HTML report (
Reports > Notes).
- Generate Nodes from Comments, also available in the Assessment Editor, utilizes Hellixia for analyzing expert comments collected in BEKEE. This function automatically generates a Semantic Network by identifying key dimensions mentioned in the comments. When experts provide structured lists of dimensions (e.g., using bullet points), this feature efficiently extracts and organizes them into nodes. It significantly reduces the time required for analyzing notes, creating nodes, ensuring no duplicates or equivalent concepts exist, and even generates useful descriptive comments for defining each dimension—an essential step in well-organized brainstorming sessions that is often overlooked due to time constraints. Furthermore, each expert can use their own language, as the system can process multilingual inputs, ensuring inclusivity and accessibility in collaborative knowledge elicitation.
- Send Priors: When eliciting the probability distribution of a node, Send Priors allows dispatching its current distribution as a prior to all experts in BEKEE, serving as an alternative to the default uniform distribution.
If the distribution being sent is a consensual distribution—derived from multiple expert-elicited distributions—Hellixia is used to generate an informative summary of all comments associated with these distributions.
This summary is sent along with the consensual distribution to BEKEE, ensuring that experts not only see the agreed-upon prior but also a synthesis of the justifications provided by those who have already contributed their assessments.
This workflow is the one used during our new brainstorming sessions, where we leverage multiple LLM engines as virtual experts to estimate local effects and provide justifications.
The consensus derived from these LLMs is then sent to BEKEE, allowing human experts to review both the aggregated distribution and a summary of all LLM-generated justifications, fostering a more informed and collaborative decision-making process.
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The Assessment Mode setting allows you to choose the type of assessment that will be used in BEKEE, providing flexibility in expert elicitation. Experts can now provide their input using one of the following modes:
- Probability Distribution Mode (Default) - Experts define a full probability distribution for the node.
- Mean Value Mode (New) - Experts provide a numerical value, which is then transformed into a probability distribution using the Binary Mean Method. This approach is particularly useful when eliciting independent local effects, as it allows experts to express their assessments in a more intuitive way.
- Both Modes - Experts can choose between defining a probability distribution or providing a numerical mean value that will be converted accordingly.
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Node Contextual Menu:
- Generate from Assessments: this function facilitates the creation of distributions based on the weighted votes of chosen experts.
- Generate Assessments: This feature uses the node’s current probability distribution to create an assessment associated with a selected expert. If Prior Weights are linked to the node, there’s an option to use these weights to determine the expert’s confidence level in the assessments.
- Delete Zero-Confidence Assessments: this option removes all assessments in which the expert’s confidence level is set to 0.
- Delete Assessments: this feature deletes the assessments linked to the selected experts.
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Hellinger Distance: Measures the distance between experts’ votes and a reference expert (usually the consensus).
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2D/3D Mapping incorporates new metrics derived from experts’ assessments.
Formulas
The Formulas tab in the Node Editor now supports local variables.
Additionally, new functions have been introduced, such as:
TriangularMD(v1, x), i.e., triangular membership degree in fuzzy logic (under Special Functions)
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Deciban(x): The deciban is a logarithmic unit — much like the decibel or the Richter scale — introduced by Alan Turing for expressing probabilities. It is a tenth of a ban, which is also known as the base-10 log odds (under Arithmetic Functions) -
Hellinger(v1, v2): The Hellinger distance is a measure of the similarity between two probability distributions (under Inference Functions) -
NoisySum(s, leak, v1, w1, vn, wn):Used for representing situations where the variablesis the weighted (wi) sum of its parents (vi)plus an additional noise term (leak)to model uncertainty or random fluctuations -
SwitchNoisyOr(s, leak, c1, p1, cn, pn):This function implements a modified Noisy-Or model that operates based on the combined effect of allpivalues. The parameterscirepresent conditions or boolean variables, whilepiare their associated effects (positive or negative).- When the aggregated sum of
pivalues is positive, the function executes a Noisy-Or with an overall effect equal to this sum, effectively determining the probability of the True state. - Conversely, when the sum is negative, the function applies the Noisy-Or logic to the False state, adjusting the likelihood of the outcome being False according to this negative sum.
- When the aggregated sum of
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DualNoisyOr(s, leak, c1, p1, cn, pn):This function implements a variant of the Noisy-Or model where the total effect is determined by combining individual effects (pi) from boolean conditions (ci). Eachpican be positive or negative, influencing the overall outcome.The function applies an AND operation between two components:
- A Noisy-Or applied to the nodes that have a positive effect.
- The negation of a Noisy-Or applied to the nodes that have a negative effect.
This structure refines the probability computation by integrating both reinforcing and inhibiting influences.
The DualNoisyOr() function that was used by Hellixia in versions prior
to 11.6 corresponds to the current SwitchNoisyOr(). This means that if you
regenerate the probability table (via the Validate button in Node Editor \mid Probability Distribution \mid Formula), the probabilities will be updated
using the current DualNoisyOr() logic.
The SwitchNoisyOr() and DualNoisyOr() functions are automatically used by Hellixia to combine the positive and negative effects returned by the LLMs.
Use Preferences \mid Tools \mid Hellixia \mid Noisy Generator to select which of these two functions Hellixia should use.
SingleMode(v): A function designed to ascertain whether the distribution of variable v is unimodal (under Inference Functions).
Weight of Evidence
Weight of Evidence now features four new types of analyses:
- Most/Least Relevant Explanations
- Most/Least Confirmatory Clues
Structural Learning Algorithms
The EQ-based learning algorithms are now disabled in situations where the score of an arc is not the same in both directions. This can occur due to Filtered States, Constraints, Structural Priors, etc. The assumption of equivalence is no longer theoretically valid in such contexts and could result in invalid networks including cycles.
Evidence Scenario Files
- The dataset associated with a network can now be exported into an Evidence Scenario file.
- Scenarios are now editable, allowing adjustments to the index, weight, and comments.
- A new Evidence Scenario Report is now available, offering a detailed description of the scenarios’ content.
Target Evaluation
The redesigned Target Evaluation function now features dedicated tabs for:
- Classification
- Posterior Probabilities
- Regression
- Triage
Graph Layout, Rendering and Editing
Dynamic Grid Layout
This innovative layout algorithm, particularly suitable for creating readable graphs featuring badges with associated comments, is also very practical for handling graphs created with Hellixia.
View Menu
Four new functions have been introduced to optimize the display of graphs. Users can now shrink or stretch graphs both vertically and horizontally, offering more flexibility in displaying the graph.
Position Menu
This new item has been introduced to enable the adjustment of the graphical layers of Nodes and Notes. It’s available via their Context Menus.
Horizontal and Vertical Stacking
These new alignment tools enable the positioning of the selected nodes horizontally or vertically, aligning them with minimal space between them.
Highlight a Class
From the Context Menu of a Note, you can select a Highlight a Class and the Note will automatically adjust its size and position to include all nodes in the selected class.
Arc Editor
By double-clicking an arc, you can edit the Comment associated with the arc as well as its rendering properties.
New properties added:
- Show Comment Colors: An option that allows a color to be associated with the comment itself, rather than using the arc’s color.
- Comment Color Linked to Nodes: Allows associating two colors with the comment— the top color reflects the parent node, and the bottom color corresponds to the child node.
These enhancements improve the visual organization and clarity of arc annotations.
Arc Comments
The width, layer, and position of Arc Comments can now be modified. Additionally, an Automatic Comment Layout algorithm (View Menu) is available for positioning comments.
Color Link
This new feature, added to the Rendering Properties of Badges, Monitors, Bars, and Gauges, automatically applies a node’s associated color to the Name Background Color. Additionally, it automatically selects white for the Name Color on dark backgrounds and black on lighter ones.
Selection Zone
By pressing Z, you can create a selection zone, in which objects are always selected regardless of their current state, i.e., selected or not-selected.
Numerical Evidence Entry for Gauges and Bars
A new approach is introduced for entering numerical evidence through shift+click on a node. Utilize the M and B icons to select the Distribution Estimation Method (MinXEnt and Binary, respectively), with the three icon colors representing the Observation Type: No Fixing, Fix Mean, and Fix Probabilities, respectively.
Pseudo Root-Nodes
If a node only has Function Nodes as parents, making it a root node of its subnetwork, and the parents of these Function Nodes have fixed observed values, then the distribution of these pseudo root-nodes is also automatically set to fixed.
Boolean Conversion
Featured in the Tools menu, this function enables the conversion of selected nodes into boolean nodes.
2D Mapping
2D Mapping function has been enhanced to incorporate an additional dimension for node analysis: Font Size, supplementing the existing Node Size and Color dimensions. This enables font sizes to be proportional to the selected metric.
Node Analysis
The Node Analysis section has been enriched with numerous additional metrics, further enhancing its analysis capability:
- Mutual Information with Target Node
- Mutual Information with Target State
- Bayes Factor
- Normalized Bayes Factor
- Kullback-Leibler Divergence
- Normalized Kullback-Leibler
- Total Effect on Target
- Standardized Total Effect on Target
- Direct Effect on Target
- Standardized Direct Effect on Target
- Number of Assessments
- Assessment Completion Rate
- Maximum Assessment Divergence
- Overall Assessment Divergence
- Missing Value Rate
Comments
Comments associated with nodes are now displayed as you hover over them.
Hide Text
The option Hide Text for Ignored Nodes conceals the names of nodes that are Not Observable.
WebSimulator
The Class Selector is now available in the WebSimulator Editor, allowing users to select which nodes to display in the list of Available Nodes.
Additionally, the color of each node is now displayed, enabling quick identification of nodes. This enhancement is particularly useful for networks with a large number of nodes, improving usability and organization.
Network Tabs
The tabs associated with each network, displayed at the bottom of the main BayesiaLab window, can now be reorganized, allowing users to customize their workspace for improved efficiency and navigation.